Alternative Instagram Memes: Intersectional Community and Collaborative Storytelling in the Digital Age
Bibliographic record
Abstract
Memes are an increasingly popular medium for self-expression in a digital context. On the social media site Instagram, queer and politically-left meme creators are subverting hegemonic power dynamics to present humorous and original memes, with sincere self-representation at their core: what I designate, alternative Instagram memes. Queer people often face discrimination and social exclusion in their local communities, exacerbated by the isolating effects of the COVID-19 pandemic, which leads many to seek out and forge digital communities of their own. In this research-creation project, I analyze the themes and discourse present in alternative Instagram memes posted by myself and by my peers to examine how this content and the community around it forms a digital intimate public. The expansion of #deardiarymemes, an interactive meme project based on anonymous confessions, exemplifies how memes function as digital storytelling tools and is central to this research. Through a curated series of memes by myself and by my peers as well as 41 new #deardiarymemes, this work builds on existing meme scholarship using feminist theorypractice to present a previously unstudied aspect of meme culture: a subversive, leftist meme community sprouting from the social media site Instagram.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".